Text and Ontology Driven Clinical Decision Support System

August 1, 2012 - May 1, 2013

In this work, we discuss our ongoing research in the domain of text and ontology driven clinical
decision support system. The proposed framework uses text analytics to extract clinical entities
from electronic health records and semantic web analytics to generate a domain specific
knowledge base (KB) of patients‟ clinical facts. Clinical Rules expressed in the Semantic Web
Language OWL are used to reason over the KB to infer additional facts about the patient. The
KB is then queried to provide clinically relevant information to the physicians
We propose a generic text and ontology driven information extraction framework which will be
useful in clinical decision support systems. In the first phase, standard preprocessing techniques
such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the
raw data. In the second phase, a domain specific medical ontology is used to establish relation
between the extracted clinical terms. The output of this phase is a Resource Description
Framework KB that stores all possible medical facts about the patient. In the final phase, an
OWL reasoner and clinical rules are used to infer additional facts about patient and generate a
richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of
concept of this framework, we have used discharge summaries from the cardiovascular domain
and determined the TIMI Risk Score and San Francisco Syncope Score for a patient.
The goal of this research is to combine factual knowledge about patients, procedural knowledge
(clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision
support system.